Latent Variable Analysis Book

Book Information

• The book is published by Sage Publications, Inc. in 1987.
• The book is printed in the United States of America.
• All rights are reserved, and no part of the book may be reproduced or utilized without permission.

Contents

• The book contains a series editor's introduction.
• The book is divided into sections, including "The Logic of Latent Variables", "Latent Class Analysis", "Estimating Latent Categorical Variables", "Exploratory Latent Class Analyses", and "Confirmatory Latent Class Analysis".
• The book also covers "Analyzing Scale Response Patterns", "Models with Errors of Measurement", "Goodman's Scale Model", "Latent Structures Among Groups", and "Comparing Latent Structures Among Groups".
• The book concludes with "Conclusions" and has two appendices, "Appendix A" and "Appendix B".

Citations and References

• The book provides guidelines for citing university papers, including the use of proper form and inclusion of the paper number.
• The book suggests adapting one of the following formats for citing university papers: (1) IVERSEN, GUDMUND R., and NORPOTH, HELMUT. (1976) "Analysis of Variance." Sage University Paper series on Quantitative Applications in the Social Sciences, 07-001. Beverly Hills: Sage Pubns. or (2) Iversen, Gudmund R., and Norpoth, Helmut. 1976. "Analysis of Variance." Sage University Paper series on Quantitative Applications in the Social Sciences, 07-001. Beverly Hills: Sage Pubns.

Acknowledgments

• The author gratefully acknowledges the helpful comments of Lisa Crockett, William Eaton, and two anonymous reviewers.

• The data utilized in the book were made available in part by the Inter-university Consortium for Political and Social Research.### Latent Class Analysis

• Latent class analysis is a rapidly developing methodology for analyzing categorical data, introduced by Allan L. McCutcheon.

• It enables the characterization of categorical latent variables from an analysis of the relationships among several categorical manifest variables.

• The method is often referred to as a "categorical data analogue" to factor analysis.

About the Author

• Allan L. McCutcheon is the author of the book on Latent Class Analysis.
• He is a professor at the University of Delaware.

Introduction

• Latent class analysis is a technique that can be used to reduce a set of several categorical variables into a single latent variable with underlying types or "classes".
• The method can be used both as an exploratory and confirmatory technique.
• As a confirmatory technique, it can be used to test hypotheses regarding the structure of the relationships among the observed variables.

The Logic of Latent Variables

• Latent variables are unobserved variables that cannot be directly observed, such as authoritarianism, prejudice, alienation, or anomie.
• There are hundreds of other theoretically interesting concepts for which the available measures are assumed to be imperfect indicators.

Basic Orientation

• Many concepts in the social sciences cannot be directly observed, and latent class analysis is a powerful technique for making these concepts observable.
• The technique can be used to examine the relationships among two or more categorical variables.

Applications of Latent Class Analysis

• The latent class model can be used to examine the scaling properties of a set of survey items.

• There is an extended example of American electoral participation that builds on the logic of Guttman scaling.

• The method can be used to study the patterns of interrelationships among observed indicators to understand and characterize the underlying latent variable.### Latent Variables and Survey Analysis

• A new method for comparative analysis provides better characterization of latent variables, allowing for a more powerful survey analysis.

Basics of Latent Variables

• The basic premise of latent variables is that the covariance among manifest variables is due to each manifest variable's relationship to the latent variable.
• Latent variables are not directly observed but are inferred from the relationships between manifest variables.

Analysis of Latent Variables

• Recent developments provide a range of analytic techniques for parametric causal analysis among nominal and ordinal data.
• Techniques include log-linear, logit, and probit analyses, as well as other approaches.
• These techniques allow researchers to analyze causal relationships among manifest variables and latent variables.

Latent Variable Modeling

• Latent variable models can be used to characterize the relationships between manifest variables and latent variables.
• Factor analysis is a technique used to characterize continuous latent variables by analyzing sets of continuous or dichotomous observed indicators.
• Regression analysis has contributed to the popularity of latent variable modeling.

Applications of Latent Variable Modeling

• Latent variable modeling can be used to analyze the relationships between manifest variables and latent variables in various fields, such as social sciences.
• The latent class model is a type of latent variable model that can be used to analyze discrete data.

Importance of Latent Variable Modeling

• Latent variable modeling provides a powerful tool for analyzing complex relationships between manifest variables and latent variables.
• It allows researchers to gain a deeper understanding of the underlying structures and relationships between variables.